Physics-Aware Inverse Design for Nanowire Single-Photon Avalanche Detectors via Deep Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915173595348992 |
|---|---|
| author | Zhang, Boyang Li, Zhe Wang, Zhongju Yu, Yang Tan, Hark Hoe Jagadish, Chennupati Dong, Daoyi Fu, Lan |
| author_facet | Zhang, Boyang Li, Zhe Wang, Zhongju Yu, Yang Tan, Hark Hoe Jagadish, Chennupati Dong, Daoyi Fu, Lan |
| contents | Single-photon avalanche detectors (SPADs) have enabled various applications in emerging photonic quantum information technologies in recent years. However, despite many efforts to improve SPAD's performance, the design of SPADs remained largely an iterative and time-consuming process where a designer makes educated guesses of a device structure based on empirical reasoning and solves the semiconductor drift-diffusion model for it. In contrast, the inverse problem, i.e., directly inferring a structure needed to achieve desired performance, which is of ultimate interest to designers, remains an unsolved problem. We propose a novel physics-aware inverse design workflow for SPADs using a deep learning model and demonstrate it with an example of finding the key parameters of semiconductor nanowires constituting the unit cell of an SPAD, given target photon detection efficiency. Our inverse design workflow is not restricted to the case demonstrated and can be applied to design conventional planar structure-based SPADs, photodetectors, and solar cells. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18857 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Aware Inverse Design for Nanowire Single-Photon Avalanche Detectors via Deep Learning Zhang, Boyang Li, Zhe Wang, Zhongju Yu, Yang Tan, Hark Hoe Jagadish, Chennupati Dong, Daoyi Fu, Lan Applied Physics Materials Science Single-photon avalanche detectors (SPADs) have enabled various applications in emerging photonic quantum information technologies in recent years. However, despite many efforts to improve SPAD's performance, the design of SPADs remained largely an iterative and time-consuming process where a designer makes educated guesses of a device structure based on empirical reasoning and solves the semiconductor drift-diffusion model for it. In contrast, the inverse problem, i.e., directly inferring a structure needed to achieve desired performance, which is of ultimate interest to designers, remains an unsolved problem. We propose a novel physics-aware inverse design workflow for SPADs using a deep learning model and demonstrate it with an example of finding the key parameters of semiconductor nanowires constituting the unit cell of an SPAD, given target photon detection efficiency. Our inverse design workflow is not restricted to the case demonstrated and can be applied to design conventional planar structure-based SPADs, photodetectors, and solar cells. |
| title | Physics-Aware Inverse Design for Nanowire Single-Photon Avalanche Detectors via Deep Learning |
| topic | Applied Physics Materials Science |
| url | https://arxiv.org/abs/2502.18857 |